There’s data, there’s information, and then there’s knowledge. Knowledge, or insight, is where we make sense out of and organize information that we have pieced together from what was just random, unstructured and otherwise useless data.

Data visualization is how we describe this process by presenting data in a visual representation somewhat more advanced than simple spreadsheets, such as interactive infographics, maps, graphs and charts, making the data analysis process considerably easier.

This allows for complex data groupings or concepts like patterns and relative connotations to be quickly digested. In this way, decision makers can have easier time pinpointing things which might benefit by an attention aimed at amendment,  identify elements impacting consumer behaviors, determine more efficient product positioning strategies, and forecast potential gross sales revenues. The way this process benefits companies is that big data analyses can allow for identification of new avenues for generating revenue, more effective and targeted marketing campaigns, improved customer service, better organizational efficiency, and, potentially, a competitive edge over contenders in the marketplace.

The Big Data Analytical Process

Data visualization is the step in the big data analysis process wherein data has been gathered, usually a whole lot of it, in a process called data scraping, where web crawler bots scrape data from websites from a large variety of sources for a specific project, filtered, analyzed, and aggregated for interpretation. Once data has been scraped from a website, it is warehoused until it can be sorted through, filtered, and analyzed. At this point, the role of quality data management systems becomes critical in the analytical process.

Data to be warehoused then needs to be organized and divided and parsed for an efficient job of extraction, transformation, and loading integration and analysis. Then, when it is optimized in this way, it can be analyzed with common advanced analytics processing software. This can include data mining tools, which comb through sets of data looking for patterns and correlations. Predictive analysis can also be a part of the process, which develops models for forecasting customer behavior. Also of import is machine learning, which is where algorithms are used to analyze voluminous sets of data. Text mining and software which analyze statistics can further be components in the process of big data analysis.

Visualization Formats

Infographics are graphics that contain copy, data and visual representations, which simultaneously operate to explain what the data means in its organized form. Infographics are great tools to get a concept across in an easy manner that is also quite plain and elementary. They are great for developing brand recognition since they can be easily and quickly shared and are visually attractive that are highly good for social marketing. Sometimes, the infographics are animated and/or interactive. Animated infographics involve animation and motion, but are not interactive in the sense that the audience can’t manipulate the animation or motions in the infographic itself and the data it presents.

Animated infographics incorporate the same advantages as regular infographics, but these involve motion, which is more engaging for viewers and can be good alternatives to using interactive or motion graphics. They are great for social, editorial, and advert purposes. Interactive infographics include online material or data that viewers are allowed to interact with, such as scrolling, clicking, zooming in or out or in any other way engaging the data presented on the screen. Motion graphics are animated graphics that feature presentations through animated visuals and kinetic typography.

Interactive infographics are useful for occasions where you have a very large amount of data that needs to be easily accessible. They attain and maintain viewer attention by requiring their input, and involves the audience where through the time and familiarity and engagement with its content, the viewers are more likely to retain and understand it. They’re most useful when exploring a high volume of data and need the audience to delve deeply into the story. And last, but not least, are videos, live films that can include text, graphics and other components to tell the story. Video offers a chance to show creative thinking and involve actors doing the storytelling, so that the presentation format can have that human like feel to it. They’re great for a short story in a short time frame, and great when you want to make an emotional connection with the audience.

Challenges Faced by the Data Visualization Process

Today’s business market forces the need to find and analyze data in record busting time frames in order to remain competitive. Data visualization brings first aid to the scene by providing the abilities to analyze data more quickly and, in so doing, make important decisions at a faster pace. The challenge this faces is the intense volume of data and the ability to pour through it quickly and accurately.

In addition, a considerable degree of perception is needed to organize the data in a manner that is useful enough to be included in a visualization. This might be the case if, for analogy, the data were pulled from social media and the goal is to identify popular product trends by monitoring what the buzz is on it among the differing demographics and the particular goal for that specific visualization might be.

Once you are able to locate and process the data fast enough, and place it in the settings needed to assure effective digestion by the target audience, it is necessary for its effectiveness that the data be presented in an organized fashion in a systematic and punctual manner. This is a common issue with big data projects that many experience when dealing with the sheer volume of data to be sifted through.

However, visualization as an important aspect in the process of data analysis is only as useful as the accuracy and quality of the data that is involved. This can also be rendered more complex when the process involves massive amounts of data from a diverse range of informative categories.  Data scraping allows you to display data in customized fields, thus allowing better visualization.

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